I am using GPT-Neo model from transformers to generate text. Because the prompt I use starts with '{', so I would like to stop the sentence once the paring '}'
I have succesfully trained a text emotion classifier fine-tuning a RoBERTa language model, mostly using a helpful notebook found online. Now I am trying to writ
It's my first time with SageMaker, and I'm having issues when trying to execute this script I took from this Huggingface model (deploy tab) from sagemaker.huggi
I am using sentiment-analysis pipeline as described here. from transformers import pipeline classifier = pipeline('sentiment-analysis') It's failing with a con
I would like to load a custom dataset from csv using huggingfaces-transformers
i find a answer of training model from scratch in this question: How to train BERT from scratch on a new domain for both MLM and NSP? one answer use Trainer and
I am fine tuning a BERT model for a multiclass classification task. My problem is that I don't know how to add "early stopping" to those Trainer instances. Any
I have successfully installed transformers package in my Jupyter Notebook from Anaconda administrator console using the command 'conda install -c conda-forge tr
I have some custom data I want to use to further pre-train the BERT model. I’ve tried the two following approaches so far: Starting with a pre-trained BER
I am using Anaconda, python 3.7, windows 10. I tried to install transformers by https://huggingface.co/transformers/ on my env. I am aware that I must have eith
a pip freeze yields the following for hugging face transformers: git+https://github.com/huggingface/transformers.git@8ddbfe975264a94f124684a138a2a5ca89a2bd0d
I am trying to explore T5 this is the code !pip install transformers from transformers import T5Tokenizer, T5ForConditionalGeneration qa_input = """question: Wh
Right now I have: model = GPTNeoForCausalLM.from_pretrained(model_name) tokenizer = GPT2Tokenizer.from_pretrained(model_name) input_ids = tokenizer(prompt, retu
I am running T5-base-grammar-correction for grammer correction on my dataframe with text column from happytransformer import HappyTextToText from happytransform
My dataset is only 10 thousand sentences. I run it in batches of 100, and clear the memory on each run. I manually slice the sentences to only 50 characters. Af
I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text. For example, having a pre
I am currently generating text from left context using the example script run_generation.py of the huggingface transformers library with gpt-2: $ python transf
In the HuggingFace tokenizer, applying the max_length argument specifies the length of the tokenized text. I believe it truncates the sequence to max_length-2 (
I'm trying to implement ML models with Amazon SageMaker Studio, the thing is that the model that I want to implement is from hugging face and It uses a Dataset
I am just using the huggingface transformer library and get the following message when running run_lm_finetuning.py: AttributeError: 'GPT2TokenizerFast' object